Dual transcriptome based reconstruction of Salmonella-human integrated metabolic network to screen potential drug targets

Salmonella enterica serovar Typhimurium (S. Typhimurium) is a highly adaptive pathogenic bacteria with a serious public health concern due to its increasing resistance to antibiotics. Therefore, identification of novel drug targets for S. Typhimurium is crucial. Here, we first created a pathogen-host integrated genome-scale metabolic network by combining the metabolic models of human and S. Typhimurium, which we further tailored to the pathogenic state by the integration of dual transcriptome data. The integrated metabolic model enabled simultaneous investigation of metabolic alterations in human cells and S. Typhimurium during infection. Then, we used the tailored pathogen-host integrated genome-scale metabolic network to predict essential genes in the pathogen, which are candidate novel drug targets to inhibit infection. Drug target prioritization procedure was applied to these targets, and pabB was chosen as a putative drug target. It has an essential role in 4-aminobenzoic acid (PABA) synthesis, which is an essential biomolecule for many pathogens. A structure based virtual screening was applied through docking simulations to predict candidate compounds that eliminate S. Typhimurium infection by inhibiting pabB. To our knowledge, this is the first comprehensive study for predicting drug targets and drug like molecules by using pathogen-host integrated genome-scale models, dual RNA-seq data and structure-based virtual screening protocols. This framework will be useful in proposing novel drug targets and drugs for antibiotic-resistant pathogens.

We are grateful to the reviewer for this comment, which enabled us to provide an additional source of validation for our pathogen-host integrated genome-scale metabolic model. Predicted essential genes were checked by using Database of Essential Genes (DEG), which reports data from three experimental gene deletion studies from rich medium experiments (Barquist et al., 2013;Khatiwara et al., 2012;Knuth et al., 2004). The results were provided in the revised manuscript under the section "Identification of Potential Drug Targets", first paragraph and 3 rd paragraph. Data were available for 137 of 140 predicted essential genes, 93 of which were reported as essential genes in at least one study (68%). Regarding the prioritized list of 28 essential genes, 20 were reported as essential in the database. When we ranked the prioritized list in terms of broad spectrum score (number of pathogenic bacteria with significantly similar sequence of the gene), 16 of the top 20 genes were essential based on the DEG database (80%). Considering that the experiments were performed in rich medium conditions, with no host cells involved, we believe the predictions provide additional validation of the pathogen-host integrated genome-scale metabolic network. We have also added a new column to Table 3, indicating whether the predicted drug target is reported to be essential in DEG.
3) The prioritization criteria for selecting pabB from 28 potential drug targets -that are nonhomologous to human proteins, druggable and broadly distributed among other bacteria, is only based on the importance for PABA. This should be justified by mentioning how they arrived at one candidate gene based on the model predictions, for e.g., effect in biomass within host (% inhibition) or participation in higher number of essential reactions in the network?
All of the proposed candidate drug targets were selected based on their effect on biomass production. Inhibition of all the proposed 28 candidate drug targets leads to blocking of biomass production (zero growth rate). pabB is also reported as an essential gene in DEG database. 20 of 28 candidate targets were experimentally verified in the DEG database. When we rank those 20 genes in terms of broad spectrum behaviour, pabB is 7 th in terms of number of pathogenic bacteria strains that carry a gene with high sequence similarity. The reaction associated with pabB is related to production of nucleic acids thymine and adenine as well as production of numerous amino acids in the model. Therefore, we chose pabB for docking analysis. The network that shows the relationship of pabB with critical metabolites was given as Supplementary Figure S1 in the revised manuscript (please also see our reply to the next comment). Those explanations are now included in the revised manuscript, Section "Identification of potential drugs for pabB", as given below. We thank the reviewer for helping us clearing up the ambiguity associated with this issue in our manuscript.
"Additionally, we investigated the importance of pabB in the pathogen-host integrated GMN model. Interactions of the metabolites of the reaction (chorismate, 4-amino-4-deoxychorismate, L-glutamate, L-glutamine) with the metabolites of other reactions were visualized by creating a metabolite-metabolite interaction network (S1 Fig.). L-glutamate is directly related to numerous amino acids such as alanine, leucine and asparagine. On the other hand, 4-amino-4deoxychorismate is indirectly related with the production of thymidine through tetrahydrofolate. It is also linked with the production of adenine through R-Pantoate. Therefore, DNA synthesis is dependent on the production of 4-amino-4-deoxychorismate through adenine and thymine synthesis, which cannot be synthesized when pabB is inhibited." 4) Metabolic modeling could be used to track the mechanism of a knockout phenotype. Could the authors sketch out the mechanism by which pabB is becoming essential in Salmonella? via metabolic networkdelineating the reactions involving pabB and the effect of deleting pabB in Salmonella? How many metabolic reactions in pathogen network are linked to pabB and/or other gene targets identified? How many of these reactions become essential reactions for the pathogen within host?
We thank again the reviewer for this comment. We investigated the effect of inhibition of pabB through metabolite-metabolite interaction network, which is indeed a feature implemented in COBRA Toolbox by one of the authors (K. Kocabas). The network is given as a supplementary figure for the revised manuscript. The results and comments were provided in the revised manuscript under the section "Identification of potential drugs for pabB". Briefly, we tracked the metabolites that were affected from to function of pabB. Since the associated reaction is related to several amino acids as well as two nucleic acids, its blocking inhibits macromolecular synthesis, leading to zero growth.

5)
Line 383, Equation 1 -α and β are calculated via FBA and then used as weights. Will it be limited to the constraints used each time the FBA is run or is fixed for all simulations? Instead, could FBA with two objective functions (HB and PB) at the same time, by changing objective function weights would be more appropriate?
We used fixed α and β in all simulations. We deliberately did not change the objective function since then it would be ambiguous to identify the source of change.. Table 4, requires more explanation on the status of their current applicationsany homology with natural products, or in clinical trial or used in other bacterial screening etc., in order to increase the application of these identified compounds.

6) Top 10 compounds identified and listed in
There are not any literature reports for the biological activities of the 10 identified compounds. However, the shortlisted compounds are drug-like as they obey the Lipinski rules. In addition, these compounds were derived from drug-like library i.e. the ZINC15 database. Moreover, to focus on only drug-like compounds, we further filtered the compounds in the database and only obtained the compounds having logP value ≤ 5 and Molecular Weight ≤ 375 daltons from this database, which is stated in Materials & Methods section.

7)
Experimental screening for at least 3 compounds is required to support the claim that it can be a useful drug against Salmonella inside host cell. For e.g., Treating the Hela cells infected with Salmonella in vitro should inhibit the growth of the pathogen as predicted. Or finding the binding specificity of these compounds for pabB gene.
We agree that experimental validation would strengthen our theoretical findings of 10 candidate compounds. On the other hand, our research groups do not have any experimental lab facility, we are purely computational labs. We believe our major contribution to the scientific field with this work is (i) to provide a validated pathogen-host genome-scale metabolic network for S. Typhimurium, (ii) to demonstrate the integrative analysis of dual RNA-Seq data with pathogenhost genome-scale metabolic models. We have included in the revised manuscript a statement that the compounds need experimental validation (Last sentences of section "Identification of potential drugs for pabB".
On the other hand, the following paper from our group has been well cited as well as found experimental proof within one month of this article published online: Ref#1: Identification of chymotrypsin-like protease inhibitors of SARS-CoV-2 via integrated computational approach, Journal of Biomolecular Structure and Dynamics, (2021), 39:7, 2607-2616, DOI: 10.1080/07391102.2020.1751298 We believe our theoretical findings will still help other researchers in drug development field, and will aid in the efforts to design novel drugs. Figure 1 would benefit by having two panelsseparately for A) host and B) pathogen and having the column bar side by side to represent 0th and 16th hour instead of superimposing them.

8)
We thank again the reviewer for this comment. We recreated the Figure 1 as panel A) and B). We used bidirectional bar chart instead of creating a bar chart with column bar side by side to represent 0th and 16th since it otherwise led to a too big figure that contained a lot of empty spaces.

9)
The dataset used also has an 8h time point. Why was it excluded from the analysis?
We wanted to compare the beginning of the infection with the late stage of the infection. We could not see any considerable differences between 0 time point and 8h time point in terms of predicted flux values. Probably the infection does not yet show its damage to the cells in this time point. Therefore, we did not include 8h time point. We added this information in the revised manuscript under the Materials&Methods section "Transcriptome data".